user-profile-reader

name	user-profile-reader
description	Read user profile from workspace and calculate content relevance. Use to personalize output based on user interests.
tools	Read

User Profile Reader Skill

Read and interpret user preferences for content personalization.

What This Skill Does

• Reads user-profile.json from workspace root
• Provides user context to other processing
• Calculates relevance scores for content

User Profile Location

~/.looplia/user-profile.json

User Profile Schema

{
  "userId": "string",
  "topics": [
    { "topic": "string", "interestLevel": 1-5 }
  ],
  "style": {
    "tone": "beginner" | "intermediate" | "expert" | "mixed",
    "targetWordCount": 100-10000,
    "voice": "first-person" | "third-person" | "instructional"
  }
}

Relevance Scoring Algorithm

Calculate score.relevanceToUser (0-1):

1. For each user topic:
   - weight = interestLevel / 5
   - matched = content tags/themes contain topic (case-insensitive)

2. Calculate score:
   - matchedWeight = sum of weights for matched topics
   - totalWeight = sum of all topic weights
   - score = matchedWeight / totalWeight

3. If no user topics defined:
   - score = 0.5 (neutral)

Example Calculation

User profile:

{
  "topics": [
    { "topic": "AI", "interestLevel": 5 },
    { "topic": "productivity", "interestLevel": 3 },
    { "topic": "cooking", "interestLevel": 2 }
  ]
}

Content tags: ["AI", "safety", "alignment"]

Calculation:

• AI: matched, weight = 5/5 = 1.0 (contributes to matchedWeight)
• productivity: not matched, weight = 3/5 = 0.6 (contributes to totalWeight only)
• cooking: not matched, weight = 2/5 = 0.4 (contributes to totalWeight only)
• matchedWeight = 1.0
• totalWeight = 1.0 + 0.6 + 0.4 = 2.0
• score = 1.0 / 2.0 = 0.5

Usage in Other Skills

When content-documenter needs relevance score:

1. Read user-profile.json
2. Compare content tags/themes to user topics
3. Apply algorithm above
4. Return score in score.relevanceToUser field

Handling Edge Cases

• No user profile file: Use score = 0.5
• Empty topics array: Use score = 0.5
• Invalid JSON: Use score = 0.5, log warning
• All topics matched: score = 1.0
• No topics matched: score = 0.0